• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.10 no.1
• /
• pp.1-8
• /
• 2017

In this paper we present a spatial big data query processing system that can store spatial data in Hadoop and query the data with SQL-based query language. The system stores large-scale spatial data in HDFS-based storage system, and supports spatial queries expressed in SQL-based query language extended for spatial data processing. It supports standard spatial data types and functions defined in OGC simple feature model in the query language. This paper presents the development of core functions of the system including query language parsing, query validation, query planning, and connection with storage system. We compares the performance of the suggested system with an existing system, and our experiments show that the system shows about 58% performance improvement of query execution time over the existing system when executing region query for spatial data stored in Hadoop.

• Journal of Korean Society for Geospatial Information Science
• /
• v.14 no.2 s.36
• /
• pp.69-75
• /
• 2006

This study aims to standardize the work classification system in spatial data. It is the base frame to classify the general information systematically in spatial data construction process. Work process of the surveying firm and rules for basic surveying which is being accomplished in the NGII(National Geographic Information Institute) are investigated and analysed. Therefore, types, individual process, and results of surveying work is standardized. If the work classification system from this study is adopted as the national standard and is also advanced by construction methodology, the spatial data will be managed futuristically and systematically.

• Architectural research
• /
• v.17 no.2
• /
• pp.57-64
• /
• 2015

In architectural planning, previous cases of similar spatial program provide important data for architectural design. Case-based reasoning (CBR) paradigm in the field of architectural design is closely related to the designing behavior of a planner who makes use of similar architectural designs and spatial programs in the past. In CBR, spatial graph can be constituted with most fundamental data, which can provide a method of searching spatial program by using visual graphs. This study developed a system for CBR that can analyze the similarity through graph comparison and search for buildings. This is an integrated system that is able to compare space similarity of different buildings and analyze their types, in addition to the analysis on a space within a single structure.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.11 no.1
• /
• pp.155-166
• /
• 2008

The quantitative bivariate spatial pattern analysis was applied for the water quality and nutrients data of Masan Bay, and for this analysis Pearson's r as aspatial correlation measurement, Moran's I as spatial association measurement and L index as integration of aspatial and spatial measurement methods were used. To understand the aspatial and spatial characteristics implicated in L index, Pearson's r as well as Moran's I were classified into 3 types respectively, and Pearson's r and Moran's I were combined with 9 types, and also quantile of L index value was used for each of those 9 types. Finally, these types were defined as 5 groups having not overlapped L index range. According to the application result of L index groups, bivariate water quality and nutrients showed no aspatial correlation regardless of spatial association in February and July, but they showed aspatial correlation having clustered spatial pattern in May and November. The result of this study providing the guideline for the interpretation of aspatial correlation and spatial association using L index is expected to be helpful for the marine environment pattern analysis using quantitative index for further study.

• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.259-261
• /
• 2008

GIS-based spatial data integration tasks have used exhaustive thematic maps generated from sparsely sampled data or satellite-based exhaustive data. Due to a simplification of reality and error in mapping procedures, such spatial data are usually imperfect and of different accuracy. The objective of this study is to carry out a sensitivity analysis in connection with input topographic data for landslide hazard mapping. Two different types of elevation estimates, elevation spot heights and a DEM from ASTER stereo images are considered. The geostatistical framework of kriging is applied for generating more reliable elevation estimates from both sparse elevation spot heights and exhaustive ASTER-based elevation values. The effects of different accuracy arising from different terrain-related maps on the prediction performance of landslide hazard are illustrated from a case study of Boeun, Korea.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.6
• /
• pp.851-859
• /
• 2017

Today, with the advent and development of Social Network Service (SNS), various types of information that have been difficult to observe have been pouring out. Recently, Vertical Social Networking Service (SNS), a service that shares specific interests with users' Vertical Social Networking Service) is emerging as a major research area. Especially, various human, social and spatial characteristics can be observed through geolocation data and social data collected through mobile GPS, and it is used in various studies. In this study, we analyze the social data collected through the image - based vertical SNS Instagram, and measure the user 's experience based on the social media based on the user' s spatial context. Therefore, in this study, we investigate what types of spatial patterns exist between experiential elements of sharing experiences and geographical characteristics through social data, and examine a new model of shared experience structure through extracted data.

• Journal of Cadastre & Land InformatiX
• /
• v.51 no.2
• /
• pp.5-20
• /
• 2021

With the available national spatial information that started from the sinkholes that occurred nationwide in 2014 and integrated 15 areas of underground information, the Underground Spatial Integrated Map has been continuously maintained since 2015. However, until recently, as disasters and accidents in underground spaces such as hot water pipes rupture, cable tunnel fires, and ground subsidence continue to occur, there is an increasing demand for quality improvement of underground information. Thus, this paper attempted to prepare a plan to improve the quality of the Underground Spatial Integrated Map data. In particular, among the 15 types of underground information managed through the Underground Spatial Integrated Map, quality validation improvement measures were proposed for underground facility (structure-type) data, which has the highest proportion of new constructions. To improve the current inspection methods that primarily rely on visual inspection, we elaborate on and subdivide the current quality inspection standards. Specifically, we present an approach for software-based automated inspection of databases, including graphics and attribute information, by adding three quality inspection items, namely, quality inspection methods, rules, and flow diagram, solvable error types, to the current four quality inspection items consisting of quality elements, sub-elements, detailed sub-elements, and quality inspection standards.

• Spatial Information Research
• /
• v.22 no.3
• /
• pp.89-99
• /
• 2014

The aim of this study is to represent the residential population distribution in Seoul, Korea more precisely through the dasymetric mapping method. Dasymetric mapping can be defined as a mapping method to calculate details from truncated spatial distribution of main statistical data by using ancillary data which is spatial data related to the main data. In this research, there are two types of data used for dasymetric mapping: the population data (2010) based on a output area survey in Seoul as the main data and the building footprint data including register information as ancillary spatial data. Using the binary method, it extracts residential buildings as actual areas where residents do live in. After that, the regression method is used for calculating the weights on population density by considering the building types and their gross floor areas. Finally, it can be reproduced three-dimensional density of residential population and drew a detailed dasymetric map. As a result, this allows to extract a more realistic calculating model of population distribution and draw a more accurate map of population distribution in Seoul. Therefore, this study has an important meaning as a source which can be applied in various researches concerning regional population in the future.

• Journal of Korea Spatial Information System Society
• /
• v.1 no.2 s.2
• /
• pp.127-135
• /
• 1999

Currently, many geographic information systems deal with only vector data. Therefore, handling raster data for various types of information is greatly needed. In this paper, we design and implement a raster processor which handles spatial information in a spatial object management system called Object Management system for Geospatial Application (OMEGA). We define the data structure of the raster information to be stored in the spatial object management system and implement it to a class library in order for use in making an application program. Furthermore, a query operation and related module is implemented to extract raster information. They are then imbedded in the query executer to process a query of the raster information. Simulation results are given.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.4
• /
• pp.147-153
• /
• 2017

The mortality rate in industrial accidents in South Korea was 11 per 100,000 workers in 2015. It's five times higher than the OECD average. Economic losses due to industrial accidents continue to grow, reaching 19 trillion won much more than natural disaster losses equivalent to 1.1 trillion won. It requires fundamental changes according to industrial safety management. In this study, We classified the risk of accidents in industrial complex of Ulju-gun using spatial analytics and data mining. We collected 119 data on accident data, factory characteristics data, company information such as sales amount, capital stock, building information, weather information, official land price, etc. Through the pre-processing and data convergence process, the analysis dataset was constructed. Then we conducted geographically weighted regression with spatial factors affecting fire incidents and calculated the risk of fire accidents with analytical model for combining Boosting and CART (Classification and Regression Tree). We drew the main factors that affect the fire accident. The drawn main factors are deterioration of buildings, capital stock, employee number, officially assessed land price and height of building. Finally the predicted accident rates were divided into four class (risk category-alert, hazard, caution, and attention) with Jenks Natural Breaks Classification. It is divided by seeking to minimize each class's average deviation from the class mean, while maximizing each class's deviation from the means of the other groups. As the analysis results were also visualized on maps, the danger zone can be intuitively checked. It is judged to be available in different policy decisions for different types, such as those used by different types of risk ratings.